Augmenting End-to-End Steering Angle Prediction with CAN Bus Data

In recent years, end to end steering prediction for autonomous vehicles has become a major area of research. The primary method for achieving end to end steering was to use computer vision models on a live feed of video data. However, to further increase accuracy, many companies have added data from light detection and ranging (LiDAR) and or radar sensors through sensor fusion. However, the addition of lasers and sensors comes at a high financial cost. In this paper, I address both of these issues by increasing the accuracy of the computer vision models without the increased cost of using LiDAR and or sensors. I achieved this by improving the accuracy of computer vision models by sensor fusing CAN bus data, a vehicle protocol, with video data. CAN bus data is a rich source of information about the vehicle's state, including its speed, steering angle, and acceleration. By fusing this data with video data, the accuracy of the computer vision model's predictions can be improved. When I trained the model without CAN bus data, I obtained an RMSE of 0.02492, while the model trained with the CAN bus data achieved an RMSE of 0.01970. This finding indicates that fusing CAN Bus data with video data can reduce the computer vision model's prediction error by 20% with some models decreasing the error by 80%.Comment: 5 page

Memory-Efficient Adaptive Optimization

Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling. However, these methods maintain second-order statistics for each parameter, thus introducing significant memory overheads that restrict the size of the model being used as well as the number of examples in a mini-batch. We describe an effective and flexible adaptive optimization method with greatly reduced memory overhead. Our method retains the benefits of per-parameter adaptivity while allowing significantly larger models and batch sizes. We give convergence guarantees for our method, and demonstrate its effectiveness in training very large translation and language models with up to 2-fold speedups compared to the state-of-the-art

Exploring the Role of Emotion Regulation Difficulties in the Assessment of Mental Disorders

Several studies have been reported in the literature for the automatic detection of mental disorders. It is reported that mental disorders are highly correlated. The exploration of this fact for the automatic detection of mental disorders is yet to explore. Emotion regulation difficulties (ERD) characterize several mental disorders. Motivated by that, we investigated the use of ERD for the detection of two opted mental disorders in this study. For this, we have collected audio-video data of human subjects while conversing with a computer agent based on a specific questionnaire. Subsequently, a subject's responses are collected to obtain the ground truths of the audio-video data of that subject. The results indicate that the ERD can be used as an intermediate representation of audio-video data for detecting mental disorders

CPDA-1 Stored Blood Induced Effect on Hematological and Biochemical Parameter up to 28 Days

Introduction: When blood is stored outside the body, some hematological and biochemical changes take place resulting in reduced red blood cells survival which is an important drawback when transfused into the circulation of a recipient. Objective: The stability of hematological parameters like RBC count, WBC count, differential count, platelet count, MCV, MCH, MCHC and biochemical parameters like S. Sodium, S. Potassium, S. Chloride and albumin during extended storage at 4°C for up to 28 days was evaluated. Materials and Methods: The present research was conducted in L.N. Medical College and J.K. Hospital, Bhopal, in collaboration with blood bank department of our institute. 450 mL of blood was drawn from 30 healthy volunteer donors into citrate phosphate dextrose adenine (CPDA-1) anticoagulant (63 mL). The blood was kept for 28 days and samples were evaluated on days 1, 7, 14, 21 and 28. Results: Among the hematological parameters, there was a constant decline in WBC and platelet counts from day 0 to 28. RBC count, Hb, MCV, HCT showed increasing values; MCH was almost constant, while MCHC decreased. PDW increased while PCT increased till 4th day and then decreased. Neutrophils, Eosinophils, Monocytes decreased, Basophils remained constant while lymphocytes increased. Among the biochemical parameters, values of S. Sodium decreased, S. Chloride decreased till 3rd day, increased on 4th day and then again decreased on 5th day. S. Potassium and albumin showed increasing values. Conclusion: Extended storage of blood in blood banks leads to changes in biochemical and hematological parameters of stored blood. RBC stored for a period of time at 4°C loses viability. Some may undergo spontaneous hemolysis while in storage; others lose the ability to survive in the recipient’s circulation following transfusion. The structural and biochemical changes that RBCs go through during storage are likely to contribute to adverse transfusion effects

Effect of soluble silica fertiliser on total sugar, protein, starch content along with amylase and cellulase activity in banana

Fertilisers have become an important factor used by farmers to increase yield and improve product quality. Earth’s crust carries a large amount of elemental silicon. However, silicon is not considered an essential element for plant growth therefore is not included in fertilisers. Silicon has shown to enhance the growth and productivity of various crops. The present study aimed to explore the potential of soluble silica in improving the biochemical parameters of banana (Grand naine variety). The field experiment was conducted at Ropni Vasaad village, Burhanpur District of Madhya Pradesh from August 2017 to September 2018. Silica was supplied as potassium silicate in the liquid form under the trade name AgriboosterTM. Doses were administered at the interval of one month starting from planting the tissue culture explants till harvesting the final crop. Eight treatments were designed which included three different concentration of soluble silica applied alone and with combination with compound fertilisers. Control was without any treatment. A significant increase in fresh and dry weight was observed with all the treatments. All the combinations of soluble silica resulted in significant increase in starch and total sugar content. The protein content showed significant increase with treatments consisting of soluble silica and compound fertiliser. Cellulase and amylase activity declined on treatment with soluble silica. The present study reveals that if soluble silica is either applied alone or with compound fertiliser, it can enhance the biochemical parameters and can indirectly delay ripening of banana by altering activity of cellulase and amylase